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Colin PalmerView profile

Research Fellow

• social vision
• sensory processing
• face perception
• +7 more

Dr. Colin Palmer is a Visiting Fellow in the School of Psychology at the University of New South Wales (UNSW), where he conducts research on visual perception with a focus on social features of our sensory environment. His work examines how the brain processes elements like eyes, faces, and behaviors of people around us using visual psychophysics, computational modeling, and 3D graphical rendering. Dr. Palmer completed his Ph.D. in 2016 and Bachelor of Behavioural Neuroscience (Honours) in 2009, both at Monash University. His doctoral research explored how neurocognitive models of sensory processing relate to differences in sensory integration and social cognition in autism. His primary research interests center on understanding the perceptual and neural mechanisms underlying our sensitivity to dynamic social cues, particularly eye and head movements. Dr. Palmer investigates how the visual system extracts basic environmental elements (color, shape, motion) and develops a mechanistic understanding of how our experience of the social world arises from nervous system activity. His work has clinical applications for understanding sensory and social difficulties in conditions like autism and schizophrenia. Dr. Palmer's recent publications reveal a consistent focus on social vision, particularly gaze perception, face processing, and animacy detection. His research increasingly incorporates computational modeling approaches to understand visual perception mechanisms. There's a strong emphasis on how lighting and shading affect face and gaze perception, with growing attention to clinical applications for neurodevelopmental conditions. Dr. Palmer has received recognition for his work through several awards: Emerging Investigator Award, Australasian Cognitive Neuroscience Society, 2017 Postdoctoral presentation award, Australasian Cognitive Neuroscience Society, 2016 Dr. Palmer is actively involved in research supervision and teaching. He teaches PSYC 3221 Vision and Brain and is available to supervise research students. His research is supported by significant funding: ARC Discovery Project (2020-2022): "Extracting meaning from motion" ($492,000) ARC Discovery Early Career Researcher Award (2019-2021): "Human sensitivity to the dynamics of other people's eye movements" ($356,000) Experimental Psychology Society Study Visit Grant (2017): "Testing computational theories of autism spectrum disorder in the social domain" (£2,580) Dr. Palmer collaborates extensively with Professor Colin Clifford at UNSW and maintains international collaborations with researchers in the UK and Australia, particularly on projects related to autism spectrum disorders and social cognition.

• Medical Robotics
• Agricultural Robotics
• Infrastructure Robotics
• +4 more

Chao Chen is an Associate Professor at the Department of Mechanical & Aerospace Engineering, Monash University, and Director of the Laboratory of Motion Generation and Analysis (LMGA). He holds an adjunct position as Associate Professor at the Chinese University of Hong Kong. His research focuses on medical, agricultural, and infrastructure robotics, emphasizing design, dynamics, control, and intelligence. Key innovations include the Robotic Transverse Profiler (delivered to the Australian Road Research Board in 2016), the 3D Printed Prosthetic Hand (2017 Award), and the Apple Harvesting Robot (2019 Global AI Expo). Dr. Chen has secured over $2M in lead research funding and contributed to $18M in collaborative projects, supported by ARC, Collier Charitable Fund, and International Science Linkages. His awards include the 2017 Innovation Award from the Australian Hand Therapy Association and scholarships from ASME and FQRNT. Education: PhD in Mechanical Engineering (details not explicitly stated in text) Research interests span medical robotics (surgery and rehabilitation), mobile robotics with advanced mobility, and reconfigurable mechatronic systems . LMGA’s mission is to address real-world challenges via robotic technologies, targeting healthcare, agriculture, and infrastructure sectors. Recent projects include fruit harvesting systems, drug delivery sensors, and embodied AI frameworks. Publications highlight advancements in robotic perception (e.g., LiDAR-camera fusion), control systems, and tactile-enabled grasping. His work aligns with UN Sustainable Development Goals, particularly in advancing industry innovation and infrastructure (SDG 9) and sustainable agriculture (SDG 2). Awards: 1996 Dean’s Honor List (Shanghai Jiao Tong University), 2004 ASME Scholarship, 2005 FQRNT Doctorate Scholarship Grants and collaborations include leading the ALF 1 Transverse Profiler and participating in the $18M ARC Smart Process Design Hub . Current projects address robotic fruit harvesting, hydrogel drug delivery systems, and AI-driven manipulation frameworks. Labs/Teams: Director of LMGA, collaborating internationally with institutions like IRCCyN (France), Shanghai Jiao Tong University, and Agriculture Victoria.

• Electrical Engineering
• Engineering Practice and Education
• Control Engineering
• +5 more

Professor Ben Horan is the Head of School of Engineering at Deakin University , Faculty of Science Engineering and Built Environment. He holds a Doctor of Philosophy and Bachelor of Engineering from Deakin University, with expertise in electrical engineering , control systems , and human-centred computing . As a leading researcher in virtual reality (VR) applications, he focuses on safety training, aged care, and extended reality (XR) systems. PhD in Electrical Engineering (Deakin University) Graduate Certificate of Higher Education (Deakin University) Bachelor of Engineering (Deakin University) His research spans VR for electrical safety training , automated vehicle interactions , and XR applications in museums . His work includes grants from the Department of Health, Melbourne Water Corporation, and City of Greater Bendigo. Recent publications analyze 360° video realism, cognitive load in virtual workplaces, and AR for Industry 5.0. Professor Horan supervises PhD candidates exploring topics like autonomous vehicle pedestrian interactions , VR stress mitigation , and industrial XR systems . He has completed supervision of 10+ PhD and Master’s students.

Scott Anthony SissonView profile

Professor

• Computational Statistics
• Bayesian Statistics
• Statistical Extreme Value Theory
• +3 more

Professor Scott Anthony Sisson is a leading academic at the University of New South Wales (UNSW) , holding the position of Professor of Statistics and Data Science in the School of Mathematics and Statistics . He serves as Director of the UNSW Data Science Hub (uDASH) and Deputy Director of the UNSW AI Institute (UNSW.ai) . Previously, he was Deputy Director of the Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and held leadership roles in the Australasian Society of Bayesian Analysis and Statistical Society of Australia . PhD in Statistics (Bristol University, 2002) MSc in Environmental Statistics and Systems (Lancaster University, 1997) BSc in Mathematics and Statistics (Lancaster University, 1996) His research focuses on computational statistics and Bayesian inference , with expertise in machine learning , extreme value theory , and high-dimensional data analysis . He develops simulation-based algorithms for complex statistical problems and applies these to diverse scientific challenges like seagrass decline, urban flood modeling, and drug delivery systems. His recent work spans quantum computing for statistics, graphon modeling, and synthetic likelihood methods. Scientific awards include: 2024 Fellow of the International Society of Bayesian Analysis 2023 Fellow of the Institute of Mathematical Statistics 2017 ARC Future Fellowship 2010 Queen Elizabeth II Research Fellowship 2006 John Yu Fellowship His advising team has mentored students in statistical modeling, Bayesian computation, and applied data science. Grants from the Australian Research Council and industry collaborations support his research in government and scientific applications. He contributes as Associate Editor for Journal of Computational and Graphical Statistics and Statistics and Computing .

Andrew WhyteView profile

Professor

• Life-Cycle Analysis
• Civil Engineering
• Construction Project Management
• +5 more

Professor Andrew Whyte serves as a faculty member in the School of Civil and Mechanical Engineering within the Faculty of Science and Engineering at Curtin University, Perth. His expertise spans civil engineering, sustainable infrastructure development, and construction management with a particular emphasis on life-cycle assessment methodologies. Professor Whyte maintains an active research profile with numerous international collaborations, especially with Malaysian institutions and industry partners across Southeast Asia. BSc, PhD (formal qualifications) Professor at Curtin University since at least 2000 Extensive international research collaborations Active participation in professional engineering organizations Professor Whyte's research interests focus on life-cycle analysis methodologies (LCA/LCCA) applied to civil infrastructure systems, with particular emphasis on sustainable materials, construction waste management, and transportation systems. His work bridges theoretical engineering principles with practical applications in sustainable construction practices, asset management, and infrastructure development. Recent research has expanded into autonomous vehicle integration, offshore engineering systems, and sustainable urban mobility solutions. Analysis of Professor Whyte's publication record reveals a consistent trajectory toward increasingly sophisticated life-cycle assessment methodologies applied across diverse infrastructure domains. His recent work demonstrates growing integration of computational modeling techniques with environmental and economic analysis, particularly in transportation systems and offshore engineering applications. The research shows strong industry relevance with practical applications in sustainable construction practices and infrastructure asset management. FICE (Fellow of the Institution of Civil Engineers) FIEAust (Fellow of the Institution of Engineers Australia) CPEng (Chartered Professional Engineer) NER (National Engineering Register) APECEngineer IntPE(Aus) (International Professional Engineer - Australia) Professor Whyte has secured substantial research funding from diverse sources including Australian government bodies, Malaysian research councils, and industry partners. His grant portfolio demonstrates expertise in translating academic research into practical engineering solutions, with particular strength in sustainable construction practices, offshore engineering systems, and infrastructure asset management. These projects often involve multi-institutional collaborations that bridge academic research with industry implementation.

Thomas ChaffeyView profile

Lecturer

• Nonlinear Control Theory
• Convex Optimization
• Circuit Theory
• +3 more

Dr. Thomas Chaffey is a Lecturer in the School of Electrical and Computer Engineering at The University of Sydney. His research focuses on nonlinear control theory, convex optimization, and neuromorphic systems. He obtained his PhD from the University of Cambridge (2022) and held the Maudslay-Butler Fellowship at Pembroke College, Cambridge (2022–2025). Education: PhD in Control Theory, University of Cambridge (2022) Maudslay-Butler Fellowship in Engineering, Pembroke College, Cambridge (2022–2025) Master's in Mechanical Engineering, University of Sydney (Australia) Bachelor's in Mathematics and Computer Science, University of Sydney (Australia) Research Interests: Nonlinear control theory and its intersections with optimization and circuit theory Development of neuromorphic systems and analog hardware simulation Monotone operator methods and large-scale optimization algorithms Key Research Trends in Articles: Advances in graphical methods for nonlinear system analysis (e.g., scaled relative graphs) Analysis of neuromorphic circuits using convex optimization frameworks Exploration of symmetry properties in physical systems Awards: Best Student Paper Award, 2021 European Control Conference Outstanding Student Paper Award, 2021 IEEE Conference on Decision and Control Labs/Teams: Leading projects on learning in physical systems and monotone circuits Collaborations with institutions like Lund University and University of British Columbia

• Decision Support Systems
• Health Informatics
• Knowledge Management
• +3 more

Frada Burstein is an Adjunct Professor at Monash University's Department of Human Centred Computing, affiliated with the Caulfield School of IT. She holds a PhD in Decision Support Systems and has over 30 years of academic experience, specializing in decision theory, healthcare informatics, and knowledge management. She is Director of the Knowledge Management Research Program and leads a laboratory focused on improving clinical decision support and patient-centered care. Education: Doctor of Philosophy (Decision Support Systems), RAS Center for Theoretical Problems of Physicochemical Pharmacology (1984) Master of Science (Applied Mathematics), Tbilisi State University (1978) Graduate Diploma (Graphic Arts), Tbilisi State University (1978) Research Interests: Her work focuses on decision support systems for healthcare, improving user experience across devices, and knowledge management in multidisciplinary contexts. Key projects include developing clinical decision support tools for reducing unnecessary medical tests and creating digital platforms for patient-centered care. She has secured over $3M in grants, including ARC Linkage projects with IBM and the Bureau of Meteorology. Awards: ICT Educator of the Year (2013) Monash Postgraduate Supervisor of the Year (2003) IFIP Outstanding Service Award (2020) AIS Distinguished Member (2019) Grants & Projects: Current roles include co-investigator on projects like 'Nudge CDSS' for clinician decision-making and 'Improving Patient-centered Care through Digital Change.' She has supervised 28 PhD and 13 MRes students, with five ongoing doctoral co-supervisions. Labs & Teams: Leads the Knowledge Management Research Program and collaborates with industry partners on health informatics and emergency management systems.

Farshid HajatiView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +1 more

Farshid Hajati is a Lecturer in Data Science at the University of New England's School of Science and Technology. He holds a PhD from Western Sydney University and has industry experience as a Senior Data Scientist at Australian government health agencies. His expertise spans machine learning, medical AI, and computer vision. Dr. Hajati's research develops deep learning solutions for medical applications including retinal disease detection, cardiac arrhythmia classification, and fungal infection diagnosis. He has secured significant funding including $433,000 for an intracranial pressure assessment device and $100,000 from Google Research. His publications demonstrate consistent innovation in multimodal medical AI, with recent advances in interpretable graph networks for biomedical data and handheld retinal imaging. Earlier foundational work established methods for 3D face recognition and dynamic texture analysis.

Emily WrightView profile

Professor

• Design Education
• Packaging Design
• Sustainable Design
• +2 more

Emily Wright serves as Associate Dean Education at Swinburne University of Technology's School of Design and Architecture, holding a PhD from the same institution alongside a Master of Design and Bachelor of Science in Graphic Design from the University of Cincinnati. With over 20 years of international design industry experience spanning branding, packaging, publishing, environmental and web design across the USA, UK, Mexico and Australia, she bridges academic theory with professional practice. Her research specializes in design education , packaging design , and sustainable design , with particular focus on pedagogical innovation including pass/fail grading systems, generative AI integration, and co-creation methodologies. Wright's scholarly output reveals strong emphasis on Transforming design pedagogy through technology Health communication design for vulnerable populations Cross-cultural co-creation frameworks Sustainable design integration in curricula Her recent publications (2020-2023) demonstrate accelerating focus on digital learning environments, AI ethics in design education, and child/family health communication systems. Wright has received the International Design Award for her child health team visualization work and secured research contracts with major organizations including Coles Supermarkets, IRESS Limited, and Access Health Community. Her grant portfolio reflects strong industry collaboration across healthcare, retail, and financial sectors. As a doctoral supervisor, Wright has guided seven PhD candidates through projects examining design feedback practices, 3D printing in bridal fashion, urban community development, and technology-based speech learning applications. She actively contributes to design education discourse through conference leadership including editing the Cumulus 38° South proceedings.

Naeha SharifView profile

Lecturer

• Computer Vision
• Natural Language Processing
• Medical Image Analysis
• +5 more

Dr. Naeha Sharif is a Lecturer in Computer Science & Software Engineering at The University of Western Australia (UWA), specializing in Computer Vision (CV) and Natural Language Processing (NLP). She holds a PhD from UWA (2021) focused on AI-driven image captioning and received the prestigious ACS 1962 Medal for outstanding IT/CS research. Her work bridges AI with healthcare, including automated medical assessment via spine scans (DXA) for abdominal aortic calcification analysis and AI-driven medical report generation for fundus imaging. Education: PhD in Computer Science (UWA 2021), Master of Engineering in Biomedical Engineering (Kyung Hee University, 2013). Previous roles include Postdoctoral Fellow at Edith Cowan University (2021–2022). Research Interests: Vision-language models, medical image analysis, captioning metrics, multimodal learning, and AI applications in healthcare. Active in UN SDGs related to health and innovation. Teaching: Unit Coordinator for Computer Graphics & Animation (CITS3003, 2022–2023) Computational Thinking with Python (CITS1401, 2023) Grants: Lead investigator on the 'Generative AI for Screening of Depressive Disorders' project (2024). Awards: Recognized for student learning contributions (2025), best paper awards (2024/2020), and academic excellence honors (2021/2020). Labs/Teams: Member of UWA Natural & Technical Language Processing Group and IEEE Signal Processing Society.

• Deep Learning
• Medical Image Analysis
• Bio Data Analysis
• +5 more

Dr. Sicily Fung Fung Ting is a Lecturer at the School of Information Technology (SoIT), Monash University. She earned her Ph.D. in Engineering (2020) from Multimedia University, specializing in breast cancer classification and visualization using deep neural networks. Her research focuses on deep learning applications in medical image analysis, expert systems, and biomedical engineering. Notably, her work has been recognized with the 2020 WSIS Prize (Champion) and the 2023 FIT Dean's Education Excellence Award. Education: Ph.D., Engineering (2020), Multimedia University MEng.Sc., Visualisation for Early Brain Infarct Detection (2017), Multimedia University BEng (Hons), Electronics & Robotics (2014), Multimedia University Research Interests: Deep Learning for Medical Imaging Computer-Aided Detection (CAD) Telehealth Systems Biomedical Signal Processing Key Projects: FRGS 2022: RewardQNet Framework for Intracranial Aneurysm Segmentation GEMS 2025: AI-Driven Neuroimaging Tool for Aneurysm Detection Her publications span journals like Expert Systems with Applications and Computerized Medical Imaging and Graphics , with impactful contributions to breast cancer classification and brain lesion segmentation. She actively engages in academic outreach, including public lectures and industry collaborations.

• Pulsar Astrophysics
• Radio Astronomy
• Gravitational Wave Astronomy
• +3 more

Dr. Jim Palfreyman is a Research Fellow in the School of Natural Sciences at the University of Tasmania, specializing in pulsar astrophysics with a particular focus on the Vela pulsar and its glitch phenomena. Based at the Physics department and working from the Mount Pleasant Observatory, he has established himself as a leading researcher in observational pulsar astronomy through his unconventional academic journey. Dr. Palfreyman's educational background reflects his non-traditional path into academia: PhD (2018): "A Long-term Single-pulse Study of the Vela Pulsar" - University of Tasmania MSc (2012): "Consecutive Bright Pulses in the Vela pulsar" - University of Tasmania BSc(Hons) (1985): "Graphics Simulation of Solar Eclipses" - University of Tasmania After completing his undergraduate studies, Dr. Palfreyman worked in computing for decades before returning to fulfill his passion for astronomy. His research centers on the detailed observation and analysis of pulsar behavior, with special emphasis on the Vela pulsar's glitch phenomena where it suddenly speeds up in rotation. His most significant achievement came in 2016 when he captured the Vela pulsar glitch using the University of Tasmania radio telescope at Mount Pleasant - the first time individual pulses had been observed during a glitch, a feat that had eluded astronomers worldwide for 50 years. His findings were published in Nature, establishing him as a significant contributor to the field. Analysis of Dr. Palfreyman's publication record reveals a clear trajectory from pure pulsar observation toward the intersection of pulsar astronomy and gravitational wave physics. His most recent work (2022-2025) demonstrates increasing collaboration with major international gravitational wave projects like LIGO-Virgo-KAGRA, applying sophisticated statistical methods to search for gravitational wave signatures from known pulsars. This evolution reflects the growing importance of multi-messenger astronomy in understanding neutron star physics. While no specific formal awards are documented in the available information, Dr. Palfreyman's work has received significant recognition through: Publication in Nature for capturing the Vela pulsar glitch of 2016 High citation counts for his key papers (e.g., 139 citations for his Nature paper) Regular invitations for media commentary on astronomical phenomena Dr. Palfreyman actively supervises research students and has developed what he describes as "the largest single-pulse pulsar archive in the world" through continuous monitoring at the Mount Pleasant Observatory. He collaborates extensively with international teams, including the LIGO gravitational wave observatory and NICER (an observatory on the International Space Station). His research program receives funding through multiple projects examining pulsar behavior and its implications for fundamental physics, particularly the connection between pulsar glitches and gravitational wave emission. Based at the Mount Pleasant Observatory, Dr. Palfreyman leads a research team dedicated to continuous monitoring of pulsars, with particular focus on the Vela pulsar. His facility provides critical long-term data for international collaborations studying neutron star physics. The observatory's monitoring program has established a unique dataset that continues to yield important insights into pulsar behavior, especially regarding glitch phenomena and their potential connection to gravitational wave emission.

Matthew ButlerView profile

Associate Professor

• Inclusive Technologies
• Accessible Graphics
• Assistive Technologies
• +4 more

Matthew Butler is an Associate Professor in the Department of Human-Centred Computing at Monash University's Faculty of Information Technology. His research focuses on Inclusive Technologies , particularly improving access to visual information for people who are blind or have low vision (BLV) through innovations like 3D printing, conversational agents, and multisensory interfaces. He has over 20 years of academic experience and held leadership roles including Deputy Dean (Education) and Associate Dean (Learning and Teaching). Education: PhD in Computing Education (Monash University, 2010) Graduate Certificate of Higher Education (Monash University, 2006) Bachelor of Engineering (Electronic and Computer Engineering, Monash University, 1996) Research Interests: Matthew’s work emphasizes participatory design with the BLV community, addressing challenges in data exploration, orientation/mobility, arts/culture access, and education . Key projects include accessible data visualization systems, inclusive museum experiences, and tactile navigation tools. His contributions align with UN Sustainable Development Goals related to education and disability inclusion. Grants & Projects: Accessible Data Exploration and Analysis for Blind People (Australian Research Council, 2023–2027) Inclusive Gallery Experiences: Bendigo Art Gallery Project (Helen Macpherson Smith Trust, 2019–2022) Co-leads the Monash Assistive Technology and Society Centre’s Education Pillar Awards: Senior Fellow of the Higher Education Academy Labs/Teams: Active contributor to Monash’s Assistive Technology and Society research initiatives, focusing on multisensory design for disability inclusion.

Rebecca BeirneView profile

Senior Lecturer

• Television studies
• Film studies
• Gender and sexuality studies
• +7 more

Rebecca Beirne is a Senior Lecturer in Screen and Cultural Studies within the School of Humanities, Creative Industries, and Social Sciences at the University of Newcastle, Australia. She is an active academic researcher and educator whose work bridges film, media, and cultural studies with a strong emphasis on gender, sexuality, and mental health representation. Beirne earned her PhD in English from the University of Sydney in 2007 and a Bachelor of Arts (Honours) from the same institution in 2002. Her academic journey has been centered at the University of Newcastle, where she has contributed significantly to teaching and research. Her research interests include television and film studies, with a particular focus on representations of gender, sexuality, and mental health in popular culture. She is recognized as a leading scholar in lesbian representation and has recently expanded her work to analyze how mental health conditions are portrayed in television fiction. Her research explores themes such as stigma, identity, narrative construction, and the socio-political implications of media portrayals. The trends in her recent publications reveal a consistent engagement with queer media, mental health narratives, and the evolving landscape of digital and televisual storytelling. Her work spans from analyses of lesbian representation in international cinema to critical discussions on the 'supercrip' trope in mental health portrayals. Lead investigator on the 2021 CHSF Working Parents Research Relief Scheme. Co-investigator in the 2019 Multidisciplinary humanities applications of metadata and computational text analysis project. Lead on the 2018 Australia-Germany Joint Research project. Lead on the 2017 Representing Mental Health Conditions on Television study. Investigator on the 2011 Visual Media Texts teaching and learning grant. Beirne actively supervises postgraduate research, serving as principal or co-supervisor for PhD and Master’s students in cultural studies, English, and media arts. Her supervision topics include film canons, spoken word poetry, female representation in fantasy literature, and queer graphic narratives. She has also contributed to public discourse through articles on platforms like The Conversation, discussing topics such as bisexual representation and gender dynamics in film. Her work is aligned with broader academic and societal goals around inclusivity, representation, and media literacy.

Dan SteinbergView profile

Academic

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +7 more

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).